An Adaptive Multi-Domain Fusion Feature Extraction with Method HHT and CSSD

LI Ming-ai; CUI Yan; YANG Jin-fu; HAO Dong-mei

doi:10.3969/j.issn.0372-2112.2013.12.025

您当前的位置：

首页 >

文章列表页 >

An Adaptive Multi-Domain Fusion Feature Extraction with Method HHT and CSSD

更新时间：2025-07-16

- An Adaptive Multi-Domain Fusion Feature Extraction with Method HHT and CSSD
- Acta Electronica Sinica Vol. 41, Issue 12, Pages: 2479-2486(2013)
- 作者机构：
  
  1. 北京工业大学生命科学与生物工程学院,北京,100124
  2. 北京工业大学电子信息与控制工程学院,北京,100124
- 作者简介：
- 基金信息：
- DOI：10.3969/j.issn.0372-2112.2013.12.025
  CLC： R318
- Published：2013
- 稿件说明：
移动端阅览
LI Ming-ai, CUI Yan, YANG Jin-fu, et al. An Adaptive Multi-Domain Fusion Feature Extraction with Method HHT and CSSD[J]. Acta Electronica Sinica, 2013, 41(12): 2479-2486.
DOI：

LI Ming-ai, CUI Yan, YANG Jin-fu, et al. An Adaptive Multi-Domain Fusion Feature Extraction with Method HHT and CSSD[J]. Acta Electronica Sinica, 2013, 41(12): 2479-2486. DOI： 10.3969/j.issn.0372-2112.2013.12.025.

摘要

为改善运动想象脑电信号特征提取的自适应性和实时性，提出一种基于希尔伯特-黄变换（HHT）与共空域子空间分解算法（CSSD）的特征提取方法（HCSSD）.在对脑电信号进行预处理的基础上，定义一种相对距离准则优选脑电极组合；计算脑电的Hilbert瞬时能量谱和边际能量谱，以获取脑电的时-频特征，并基于CSSD提取其空域特征，采用串行特征融合策略得到脑电的时-频-空特征；设计学习矢量量化神经网络分类器，实现脑电数据分类.在训练集与测试集间隔一周且减少导联数量的情况下，基于HCSSD对左手小指和舌头的运动想象ECoG脑电数据的平均识别率为92%.实验结果表明：HCSSD在增强特征提取方法的自适应性、改善实时性的同时，提高了脑电信号识别率，为便携式BCI系统在康复领域的应用创造了条件.

Abstract

The adaptivity and real-time performance of feature extraction method are crucial in brain-computer interface.Based on Hilbert-Huang transform (HHT) and common spatial subspace decomposition (CSSD) algorithm

a novel feature extraction method

denoted as HCSSD

was proposed.Firstly

the motor imagery electroencephalography (EEG)/ electrocorticography (ECoG) was preprocessed

and a relative distance criterion was defined to select the optimal combination of channels.Secondly

Hilbert instantaneous energy spectrum and marginal energy spectrum of EEG/ECoG were calculated to extract time feature and frequency feature respectively.Then CSSD was applied to extract spatial feature.Furthermore

serial feature fusion strategy was adopted to obtain time-frequency-spatial feature.Finally

learning vector quantization neural network was designed to classify the EEG/ECoG data.The average recognition accuracy was 92% for the left small finger and tongue motor imagery ECoG tasks.Experiment results show that HCSSD can enhance the adaptivity and real-time performance of feature extraction

with the recognition accuracy improved.This method provides a new idea for the application of portable BCI system in rehabilitation field.

关键词

Keywords

references

Views

下载量

CSCD

Alert me when the article has been cited

提交

Tools

Publicity Resources

Variable Horizon Multi-Directional Scanning Method for Time Series Anomaly Detection

Low-Light Small Target Detection Method Combining Feature Fusion Enhancement and Detail Features

Object Detection Based on EIMYOLO for High-Resolution Remote Sensing Images

Research on Improved Localization Based on Real-Time Semantic Chain List Systems

Research on Dynamic Continuous Emotional Recognition of EEG Based on Improved TCNN Algorithm

Related Author

HUANG Yu-zhe

GUAN Yong-yuan

WEI Song-jie

JIANG Ze-tao

CHENG Liu-ming

YANG Jian-chen

CAO Feng

ZENG Ke-wen

Related Institution

School of Computer Science and Engineering, School of Cyber Science and Engineering, Nanjing University of Science and Technology

Guangxi Key Laboratory of Image and Graphics Processing and Intelligent Processing, Guilin University of Electronic Technology

School of Information and Technology, Shanxi University

School of Computer Science and Technology, Soochow University

School of Electronics and Information Engineering, Suzhou University of Science and Technology

⁰